NVIDIA Deep Learning Institute

The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI and accelerated computing to solve real-world problems. Designed for developers, data scientists, and researchers, DLI content is available in three formats:

Online Courses

DLI online courses teach you how to implement and deploy an end-to-end project in eight hours. Online courses can be taken anytime, anywhere, with access to a fully configured GPU-accelerated workstation in the cloud.

Online ELECTIVES

DLI electives explore how to apply a specific technology or development technique in two hours. Like full-length courses, electives can be taken anytime, anywhere, with access to GPUs in the cloud.

Instructor-Led Workshops

In-person workshops teach you how to implement and deploy an end-to-end project through hands-on training in eight hours. Offered at customer sites, conferences, and universities, full-day workshops include hands-on training and lectures delivered by DLI certified instructors.

Certification

Participants can earn certification to prove subject matter competency and support professional career growth. Certification is offered for select online courses and instructor-led workshops.

Online TRAINING

Start self-paced courses and electives anywhere, anytime with access to a fully configured GPU-accelerated workstation in the cloud.

Introduction to Deep Learning

If you’re new to deep learning, the first step is learning how to train and deploy a neural network to solve real-world problems.

Deep neural networks are better at classifying images than humans, which has implications beyond what we expect of computer vision. Learn how to convert radio frequency (RF) signals into images to detect a weak signal corrupted by noise. You’ll be trained how to:

Prerequisites: Basic Python competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations. NumPy competency including the use of ndarrays and ufuncs.

Assessment Type: Code-based

Languages: English

Price: $90

This course explores how to use Numba—the just-in-time, type-specialising Python function compiler—to accelerate Python programs to run on massively parallel NVIDIA GPUs. You’ll learn how to:

Learn the basics of OpenACC, a high-level programming language for programming on GPUs. This course is for anyone with some C/C++ experience who is interested in accelerating the performance of their applications beyond the limits of CPU-only programming. In this course, you’ll learn:

Learn how to accelerate your C/C++ or Fortran application using OpenACC to harness the massively parallel power of NVIDIA GPUs. OpenACC is a directive-based approach to computing where you provide compiler hints to accelerate your code, instead of writing the accelerator code yourself. Get started on the four-step process for accelerating applications using OpenACC:

Characterise and profile your application

Add compute directives

Add directives to optimise data movement

Optimise your application using kernel scheduling

Upon completion, you will be ready to use a profile-driven approach to rapidly accelerate your C/C++ applications using OpenACC directives.

Prerequisites: “Accelerating Applications with CUDA C/C++” or similar experience

Languages: English

Price: $30

Thrust is a parallel algorithms library loosely based on the C++ Standard Template Library. It enables developers to quickly embrace the power of parallel computing and supports multiple system back-ends such as OpenMP and Intel's Threading Building Blocks. Use Thrust to accelerate C++ through exercises that cover:

Basic Iterators, Containers, and Functions

Built-in and Custom Functors

Portability to CPU processing

Upon completion, you'll be ready to harness the power of the Thrust library to accelerate your C/C++ applications.

Explore how to transfer the look and feel of one image to another image by extracting distinct visual features. See how convolutional neural networks (CNNs) are used for feature extraction, and how these features feed into a generator to create a new image. You’ll learn how to:

Transfer the look and feel of one image to another image by extracting distinct visual features

Qualitatively determine whether a style is transferred correctly using different techniques

Use architectural innovations and training techniques for arbitrary style transfer

Upon completion, you’ll be able to use neural networks for arbitrary style transfer at a speed that's effective for video.

Prerequisites: Basic experience with CNNs and basic experience with Python

Frameworks: TensorFlow

Languages: English

Price: $30

Thanks to work being performed at the Mayo Clinic, using deep learning techniques to detect radiomics from MRI imaging has led to more effective treatments and better health outcomes for patients with brain tumors. Learn to detect the 1p19q co-deletion biomarker by:

Designing and training convolutional neural networks (CNNs)

Using imaging genomics (radiomics) to create biomarkers that identify the genomics of a disease without the use of an invasive biopsy

Exploring the radiogenomics work being done at the Mayo Clinic

Upon completion, you’ll have unique insight into the novelty and promising results of using deep learning to predict radiomics.

Prerequisites: Basic experience with CNNs and basic experience with Python

Frameworks: MXNet

Languages: English

Price: $30

Convolutional neural networks (CNNs) can be applied to medical image analysis to infer patient status from non-visible images. Learn how to train a CNN to infer the volume of the left ventricle of the human heart from time-series MRI data. You'll explore how to:

Extend a canonical 2D CNN to more complex data

Use MXNet through the standard Python API and R

Process high-dimensionality imagery that may be volumetric and have a temporal component

A generative adversarial network (GAN) is a pair of deep neural networks: a generator that creates new examples based on the training data provided and a discriminator that attempts to distinguish between genuine and simulated data. As both networks improve together, the examples created become increasingly realistic. This technology is promising for healthcare, because it can augment smaller datasets for training of traditional networks. You'll learn how to:

Generate synthetic brain MRIs

Apply GANs for segmentation

Use GANs for data augmentation to improve accuracy

Upon completion, you'll be able to apply GANs to medical imaging use cases.

Coarse-to-fine contextual memory (CFCM) is a technique developed for image segmentation using very deep architectures and incorporating features from many different scales with convolutional long short-term memory (LSTM). You’ll:

Take a deep dive into encoder-decoder architectures for medical image segmentation

Get to know common building blocks (convolutions, pooling layers, residual nets, etc.)

Investigate different strategies for skip connections

Upon completion, you'll be able to apply CFCM techniques to medical image segmentation and similar imaging tasks.

INTELLIGENT VIDEO ANALYTICS

ELECTIVES

When a trained neural network is tasked to find the answer on new data inputs, it is referred to as deployment. TensorRT is the primary tool for deployment, with various options to improve inference performance of neural networks. In this mini course, you'll:

Learn how to use giexec to run inferencing.

Use mixed precision INT8 to optimize inferencing.

Leverage custom layers API for plugins.

Upon completion, you'll know how to use TensorRT to accelerate inferencing performance for neural networks.

Bring DLI to Your Organisation.

Managers can request onsite DLI workshops at their company or organisation. Choose from fundamentals or industry-specific topics listed below.

If you’re looking for more comprehensive enterprise training, we’ll work with you to craft a package of training and lectures that meets your organisation’s unique needs. From hands-on online and onsite training to executive briefings and enterprise-level reporting, DLI can help your company transform into an AI organisation. Contact us to learn more.

The computational requirements of deep neural networks used to enable AI applications like self-driving cars are enormous. A single training cycle can take weeks on a single GPU or even years for larger datasets like those used in self-driving car research. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.

This workshop will teach you how to use multiple GPUs to train neural networks. You'll learn:

Approaches to multi-GPUs training

Algorithmic and engineering challenges to large-scale training

Key techniques used to overcome the challenges mentioned above

Upon completion, you'll be able to effectively parallelize training of deep neural networks using TensorFlow.

Exposing accelerated application potential for concurrency and exploiting it with CUDA streams

Leveraging command line and visual profiling to guide and check your work

Upon completion, you’ll be able to accelerate and optimise existing C/C++ CPU-only applications using the most essential CUDA tools and techniques. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

Prerequisites: Experience with neural networks and knowledge of financial industry

Frameworks: TensorFlow

Languages: English

Linear techniques like principal component analysis (PCA) are the workhorses of creating “eigenportfolios” for use in statistical arbitrage strategies. Other techniques using time series financial data are also prevalent. But now, trading strategies can be advanced with the power of deep neural networks.

In this workshop, you’ll learn how to:

Prepare time series data and test network performance using training and test datasets

Structure and train a long short-term memory (LSTM) network to accept vector inputs and make predictions

Use the autoencoder as anomaly detector to create an arbitrage strategy

Upon completion, you’ll be able to use time series financial data to make predictions and exploit arbitrage using neural networks.